A generalized taxonomy of explanations styles for traditional and social recommender systems

By Alexis Papadimitriou, Panagiotis Symeonidis, Yannis Manolopoulos. 2012

126 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

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Citations in the corpus

Explainable Recommendation: A Survey and New Perspectives (2018, background): “Papadimitriou et al. [2012] studied human-style, item-style, featurestyle and hybrid-style explanations in social recommender systems, and they also studied geo-social explanations that combine geographical with social data.”
The challenge of crafting intelligible intelligence (2018, background): “…systems, one of the first deployed uses of machine learning, also induced demand for explanations of their specific recommendations; the most satisfying answers combined justifications based on the user’s previous choices, ratings of similar users, and features of the items being recommended [32].”
Explaining neighborhood-based recommendations (2012, background): “Universidad de Granada, 18071.”
Intelligible Artificial Intelligence (2018, background): “Recommender systems, which were one of the first deployed uses of machine learning, also induced demand for explanations of their specific recommendations; the most satisfying answers combined justifications based on the user’s previous choices, ratings of similar users, and features of the items be”
Designing Explanation Interfaces for Transparency and Beyond (2019, methodology): “We adopted a hybrid explanation approach [8, 12], which mixed multiple visualizations to explain the details of the recommendation model.”
A systematic review and taxonomy of explanations in decision support and recommender systems (2017, background): “A number of explanation taxonomies of different granularity levels has been proposed in the literature in the past (Ye and Johnson 1995; Chandrasekaran et al. 1989; Gregor and Benbasat 1999; Lacave and Díez 2002, 2004; Nakatsu 2006; Papadimitriou et al. 2012; Vig et al. 2009; Blanco et al. 2012; Fri”
Recommender systems survey (2013, background): “The recommendation explanation field has been investigated with new developments in RS [91] until now [170].”
Who Also Likes It? Generating the Most Persuasive Social Explanations in Recommender Systems (2014, background): “Given a set of users U and a set of items I , for recommending an item i ∈ I to a user u ∈ U , we define a social explanation as the statement ”Users e1,e2,...,ek also like item i”, with given k.”
Explainable Artificial Intelligence: a Systematic Review (2020, background): “For instance, [34] showed that rich explanations, based on a combination of information regarding users, items and features, are very effective, while [33] claimed that explanations should be context-specific to be effective.”
A Survey of Graph Neural Networks for Social Recommender Systems (2022, background): “Most of the existing surveys, which fully cover SocialRS papers, focus either on traditional meth-ods [7, 14, 68, 77, 87, 113, 118] (e.g., MF), feature information [79] (e.g., context), or a specific application [21] (e.g., CD).”
Explanations as Mechanisms for Supporting Algorithmic Transparency (2018, background): “Previous research has focused on different aspects of explanations, including data sources [42, 5], cognitive fit [22], modality (e.”